Triple
T9355975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States at the 2002 Winter Olympics |
E225140
|
entity |
| Predicate | numberOfFemaleCompetitors |
P7896
|
FINISHED |
| Object | 102 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 102 | Statement: [United States at the 2002 Winter Olympics, numberOfFemaleCompetitors, 102]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFemaleCompetitors Context triple: [United States at the 2002 Winter Olympics, numberOfFemaleCompetitors, 102]
-
A.
numberOfFemaleAthletes
chosen
Indicates the count of athletes who are female in a given context or group.
-
B.
numberOfMaleAthletes
Indicates the quantity of athletes in a given group or context who are male.
-
C.
womenMainEventParticipants
Indicates that the referenced entities are participants in a main event specifically designated for women.
-
D.
femaleMass
Indicates that the subject has a mass value specifically associated with its female form or female population.
-
E.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca842abfd48190949d71c3b86eeba8 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4fee9d4c8190a7d121c9487ccca2 |
completed | April 1, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69cc7a68ab9481909f97cb70764697cc |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:42 p.m.